PROFITH "PROmoting FITness and Health through physical activity" Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain
Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
Br J Sports Med. 2022 Apr;56(7):376-384. doi: 10.1136/bjsports-2020-103604. Epub 2021 Apr 12.
The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.
体力活动、久坐行为和睡眠（统称为身体行为）之间的相互关系引起了不同领域研究人员的兴趣。这些身体行为中的每一种都在流行病学研究中进行了研究，但它们的相互依存关系和相互作用需要在数据分析中进一步探讨和考虑。现代加速度计可以全天连续记录运动情况，这就带来了如何最好地利用这些丰富数据的挑战。近年来，最初在其他科学领域应用的分析方法已被应用于身体行为流行病学（例如，等时替代模型、组合数据分析、多元模式分析、功能数据分析和机器学习）。对这些分析方法的优缺点进行全面描述、讨论和达成共识，将有助于研究人员在不同情况下决定使用哪种方法。在这种情况下，在格拉纳达举行了一次科学研讨会和会议，讨论了以下内容：（1）目前在身体行为科学文献中使用的分析方法，强调其优缺点，提供有关其使用的实用建议，并包括一个决策树来帮助研究人员做出决策；（2）目前围绕加速度计数据的分析和使用存在的差距和未来研究方向。预计分析方法在流行病学研究中对加速度计确定的身体行为的进展将影响对当前和未来证据的解释，并最终影响未来的身体行为指南。
